"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=0.2      ,					_lowerCamelCase=0.2 ):
										lowerCamelCase__   =bp_numa
										lowerCamelCase__   =bp_numa
										lowerCamelCase__   =bp_numa
										lowerCamelCase__   =conva_get[:2]
										lowerCamelCase__   =conva_get[2]
										lowerCamelCase__   =size_pa
										lowerCamelCase__   =rate_w
										lowerCamelCase__   =rate_t
										lowerCamelCase__   =[
										    np.mat(-1 * np.random.rand(self.conva[0]      ,					self.conva[0] ) + 0.5 )
										    for i in range(self.conva[1] )
										]
										lowerCamelCase__   =np.mat(-1 * np.random.rand(self.num_bpa      ,					self.num_bpa ) + 0.5 )
										lowerCamelCase__   =np.mat(-1 * np.random.rand(self.num_bpa      ,					self.num_bpa ) + 0.5 )
										lowerCamelCase__   =-2 * np.random.rand(self.conva[1] ) + 1
										lowerCamelCase__   =-2 * np.random.rand(self.num_bpa ) + 1
										lowerCamelCase__   =-2 * np.random.rand(self.num_bpa ) + 1

				def 						_a      ( self      ,					_lowerCamelCase ):
										# save model dict with pickle
										lowerCamelCase__   ={
										    "num_bp1": self.num_bpa,
										    "num_bp2": self.num_bpa,
										    "num_bp3": self.num_bpa,
										    "conv1": self.conva,
										    "step_conv1": self.step_conva,
										    "size_pooling1": self.size_poolinga,
										    "rate_weight": self.rate_weight,
										    "rate_thre": self.rate_thre,
										    "w_conv1": self.w_conva,
										    "wkj": self.wkj,
										    "vji": self.vji,
										    "thre_conv1": self.thre_conva,
										    "thre_bp2": self.thre_bpa,
										    "thre_bp3": self.thre_bpa,
										}
										with open(_lowerCamelCase      ,					"wb" ) as f:
																pickle.dump(_lowerCamelCase      ,					_lowerCamelCase )

										print(F'''Model saved： {save_path}''' )

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase ):
										# read saved model
										with open(_lowerCamelCase      ,					"rb" ) as f:
																lowerCamelCase__   =pickle.load(_lowerCamelCase )  # noqa: S301

										lowerCamelCase__   =model_dic.get("conv1" )
										conv_get.append(model_dic.get("step_conv1" ) )
										lowerCamelCase__   =model_dic.get("size_pooling1" )
										lowerCamelCase__   =model_dic.get("num_bp1" )
										lowerCamelCase__   =model_dic.get("num_bp2" )
										lowerCamelCase__   =model_dic.get("num_bp3" )
										lowerCamelCase__   =model_dic.get("rate_weight" )
										lowerCamelCase__   =model_dic.get("rate_thre" )
										# create model instance
										lowerCamelCase__   =CNN(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
										# modify model parameter
										lowerCamelCase__   =model_dic.get("w_conv1" )
										lowerCamelCase__   =model_dic.get("wkj" )
										lowerCamelCase__   =model_dic.get("vji" )
										lowerCamelCase__   =model_dic.get("thre_conv1" )
										lowerCamelCase__   =model_dic.get("thre_bp2" )
										lowerCamelCase__   =model_dic.get("thre_bp3" )
										return conv_ins

				def 						_a      ( self      ,					_lowerCamelCase ):
										return 1 / (1 + np.exp(-1 * x ))

				def 						_a      ( self      ,					_lowerCamelCase ):
										return round(_lowerCamelCase      ,					3 )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										# convolution process
										lowerCamelCase__   =convs[0]
										lowerCamelCase__   =convs[1]
										lowerCamelCase__   =np.shape(_lowerCamelCase )[0]
										# get the data slice of original image data, data_focus
										lowerCamelCase__   =[]
										for i_focus in range(0      ,					size_data - size_conv + 1      ,					_lowerCamelCase ):
																for j_focus in range(0      ,					size_data - size_conv + 1      ,					_lowerCamelCase ):
																						lowerCamelCase__   =data[
																						    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																						]
																						data_focus.append(_lowerCamelCase )
        # calculate the feature map of every single kernel, and saved as list of matrix
										lowerCamelCase__   =[]
										lowerCamelCase__   =int((size_data - size_conv) / conv_step + 1 )
										for i_map in range(_lowerCamelCase ):
																lowerCamelCase__   =[]
																for i_focus in range(len(_lowerCamelCase ) ):
																						lowerCamelCase__   =(
																						    np.sum(np.multiply(data_focus[i_focus]      ,					w_convs[i_map] ) )
																						    - thre_convs[i_map]
																						)
																						featuremap.append(self.sig(_lowerCamelCase ) )
																lowerCamelCase__   =np.asmatrix(_lowerCamelCase ).reshape(
																    _lowerCamelCase      ,					_lowerCamelCase )
																data_featuremap.append(_lowerCamelCase )

										# expanding the data slice to One dimenssion
										lowerCamelCase__   =[]
										for each_focus in data_focus:
																focusa_list.extend(self.Expand_Mat(_lowerCamelCase ) )
										lowerCamelCase__   =np.asarray(_lowerCamelCase )
										return focus_list, data_featuremap

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase="average_pool" ):
										# pooling process
										lowerCamelCase__   =len(featuremaps[0] )
										lowerCamelCase__   =int(size_map / size_pooling )
										lowerCamelCase__   =[]
										for i_map in range(len(_lowerCamelCase ) ):
																lowerCamelCase__   =featuremaps[i_map]
																lowerCamelCase__   =[]
																for i_focus in range(0      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																						for j_focus in range(0      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																												lowerCamelCase__   =feature_map[
																												    i_focus : i_focus + size_pooling,
																												    j_focus : j_focus + size_pooling,
																												]
																												if pooling_type == "average_pool":
																																		# average pooling
																																		map_pooled.append(np.average(_lowerCamelCase ) )
																												elif pooling_type == "max_pooling":
																																		# max pooling
																																		map_pooled.append(np.max(_lowerCamelCase ) )
																lowerCamelCase__   =np.asmatrix(_lowerCamelCase ).reshape(_lowerCamelCase      ,					_lowerCamelCase )
																featuremap_pooled.append(_lowerCamelCase )
										return featuremap_pooled

				def 						_a      ( self      ,					_lowerCamelCase ):
										# expanding three dimension data to one dimension list
										lowerCamelCase__   =[]
										for i in range(len(_lowerCamelCase ) ):
																lowerCamelCase__   =np.shape(data[i] )
																lowerCamelCase__   =data[i].reshape(1      ,					shapes[0] * shapes[1] )
																lowerCamelCase__   =data_listed.getA().tolist()[0]
																data_expanded.extend(_lowerCamelCase )
										lowerCamelCase__   =np.asarray(_lowerCamelCase )
										return data_expanded

				def 						_a      ( self      ,					_lowerCamelCase ):
										# expanding matrix to one dimension list
										lowerCamelCase__   =np.asarray(_lowerCamelCase )
										lowerCamelCase__   =np.shape(_lowerCamelCase )
										lowerCamelCase__   =data_mat.reshape(1      ,					shapes[0] * shapes[1] )
										return data_expanded

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =[]
										lowerCamelCase__   =0
										for i_map in range(_lowerCamelCase ):
																lowerCamelCase__   =np.ones((size_map, size_map) )
																for i in range(0      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																						for j in range(0      ,					_lowerCamelCase      ,					_lowerCamelCase ):
																												lowerCamelCase__   =pd_pool[
																												    i_pool
																												]
																												lowerCamelCase__   =i_pool + 1
																lowerCamelCase__   =np.multiply(
																    _lowerCamelCase      ,					np.multiply(out_map[i_map]      ,					(1 - out_map[i_map]) ) )
																pd_all.append(_lowerCamelCase )
										return pd_all

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=bool ):
										# model traning
										print("----------------------Start Training-------------------------" )
										print((" - - Shape: Train_Data  ", np.shape(_lowerCamelCase )) )
										print((" - - Shape: Teach_Data  ", np.shape(_lowerCamelCase )) )
										lowerCamelCase__   =0
										lowerCamelCase__   =[]
										lowerCamelCase__   =10000
										while rp < n_repeat and mse >= error_accuracy:
																lowerCamelCase__   =0
																print(F'''-------------Learning Time {rp}--------------''' )
																for p in range(len(_lowerCamelCase ) ):
																						# print('------------Learning Image: %d--------------'%p)
																						lowerCamelCase__   =np.asmatrix(datas_train[p] )
																						lowerCamelCase__   =np.asarray(datas_teach[p] )
																						lowerCamelCase__   , lowerCamelCase__   =self.convolute(
																						    _lowerCamelCase      ,					self.conva      ,					self.w_conva      ,					self.thre_conva      ,					conv_step=self.step_conva      ,					)
																						lowerCamelCase__   =self.pooling(_lowerCamelCase      ,					self.size_poolinga )
																						lowerCamelCase__   =np.shape(_lowerCamelCase )
																						lowerCamelCase__   =self._expand(_lowerCamelCase )
																						lowerCamelCase__   =data_bp_input

																						lowerCamelCase__   =np.dot(_lowerCamelCase      ,					self.vji.T ) - self.thre_bpa
																						lowerCamelCase__   =self.sig(_lowerCamelCase )
																						lowerCamelCase__   =np.dot(_lowerCamelCase      ,					self.wkj.T ) - self.thre_bpa
																						lowerCamelCase__   =self.sig(_lowerCamelCase )

																						# --------------Model Leaning ------------------------
																						# calculate error and gradient---------------
																						lowerCamelCase__   =np.multiply(
																						    (data_teach - bp_outa)      ,					np.multiply(_lowerCamelCase      ,					(1 - bp_outa) ) )
																						lowerCamelCase__   =np.multiply(
																						    np.dot(_lowerCamelCase      ,					self.wkj )      ,					np.multiply(_lowerCamelCase      ,					(1 - bp_outa) ) )
																						lowerCamelCase__   =np.dot(_lowerCamelCase      ,					self.vji )

																						lowerCamelCase__   =pd_i_all / (self.size_poolinga * self.size_poolinga)
																						lowerCamelCase__   =pd_conva_pooled.T.getA().tolist()
																						lowerCamelCase__   =self._calculate_gradient_from_pool(
																						    _lowerCamelCase      ,					_lowerCamelCase      ,					shape_featuremapa[0]      ,					shape_featuremapa[1]      ,					self.size_poolinga      ,					)
																						# weight and threshold learning process---------
																						# convolution layer
																						for k_conv in range(self.conva[1] ):
																												lowerCamelCase__   =self._expand_mat(pd_conva_all[k_conv] )
																												lowerCamelCase__   =self.rate_weight * np.dot(_lowerCamelCase      ,					_lowerCamelCase )

																												lowerCamelCase__   =self.w_conva[k_conv] + delta_w.reshape(
																												    (self.conva[0], self.conva[0]) )

																												lowerCamelCase__   =(
																												    self.thre_conva[k_conv]
																												    - np.sum(pd_conva_all[k_conv] ) * self.rate_thre
																												)
																						# all connected layer
																						lowerCamelCase__   =self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																						lowerCamelCase__   =self.vji + pd_j_all.T * bp_outa * self.rate_weight
																						lowerCamelCase__   =self.thre_bpa - pd_k_all * self.rate_thre
																						lowerCamelCase__   =self.thre_bpa - pd_j_all * self.rate_thre
																						# calculate the sum error of all single image
																						lowerCamelCase__   =np.sum(abs(data_teach - bp_outa ) )
																						error_count += errors
																						# print('   ----Teach      ',data_teach)
																						# print('   ----BP_output  ',bp_out3)
																lowerCamelCase__   =rp + 1
																lowerCamelCase__   =error_count / patterns
																all_mse.append(_lowerCamelCase )

										def draw_error():
																lowerCamelCase__   =[error_accuracy for i in range(int(n_repeat * 1.2 ) )]
																plt.plot(_lowerCamelCase      ,					"+-" )
																plt.plot(_lowerCamelCase      ,					"r--" )
																plt.xlabel("Learning Times" )
																plt.ylabel("All_mse" )
																plt.grid(_lowerCamelCase      ,					alpha=0.5 )
																plt.show()

										print("------------------Training Complished---------------------" )
										print((" - - Training epoch: ", rp, F'''     - - Mse: {mse:.6f}''') )
										if draw_e:
																draw_error()
										return mse

				def 						_a      ( self      ,					_lowerCamelCase ):
										# model predict
										lowerCamelCase__   =[]
										print("-------------------Start Testing-------------------------" )
										print((" - - Shape: Test_Data  ", np.shape(_lowerCamelCase )) )
										for p in range(len(_lowerCamelCase ) ):
																lowerCamelCase__   =np.asmatrix(datas_test[p] )
																lowerCamelCase__   , lowerCamelCase__   =self.convolute(
																    _lowerCamelCase      ,					self.conva      ,					self.w_conva      ,					self.thre_conva      ,					conv_step=self.step_conva      ,					)
																lowerCamelCase__   =self.pooling(_lowerCamelCase      ,					self.size_poolinga )
																lowerCamelCase__   =self._expand(_lowerCamelCase )

																lowerCamelCase__   =data_bp_input
																lowerCamelCase__   =bp_outa * self.vji.T - self.thre_bpa
																lowerCamelCase__   =self.sig(_lowerCamelCase )
																lowerCamelCase__   =bp_outa * self.wkj.T - self.thre_bpa
																lowerCamelCase__   =self.sig(_lowerCamelCase )
																produce_out.extend(bp_outa.getA().tolist() )
										lowerCamelCase__   =[list(map(self.do_round      ,					_lowerCamelCase ) ) for each in produce_out]
										return np.asarray(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase ):
										# return the data of image after convoluting process so we can check it out
										lowerCamelCase__   =np.asmatrix(_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =self.convolute(
										    _lowerCamelCase      ,					self.conva      ,					self.w_conva      ,					self.thre_conva      ,					conv_step=self.step_conva      ,					)
										lowerCamelCase__   =self.pooling(_lowerCamelCase      ,					self.size_poolinga )

										return data_conveda, data_pooleda


if __name__ == "__main__":
	pass



"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			StableDiffusionLDMaDPipeline
				A__ :  List[str]    				=			TEXT_TO_IMAGE_PARAMS
				A__ :  str    				=			TEXT_TO_IMAGE_BATCH_PARAMS
				A__ :  Tuple    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
										lowerCamelCase__   =DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,					beta_end=0.0_1_2      ,					beta_schedule="scaled_linear"      ,					clip_sample=_lowerCamelCase      ,					set_alpha_to_one=_lowerCamelCase      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =AutoencoderKL(
										    block_out_channels=[32, 64]      ,					in_channels=6      ,					out_channels=6      ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,					latent_channels=4      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										lowerCamelCase__   =CLIPTextModel(_lowerCamelCase )
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

										lowerCamelCase__   ={
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
										lowerCamelCase__   =np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs["prompt"]]

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs.pop("prompt" )]

										lowerCamelCase__   =ldmad_pipe.tokenizer(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=ldmad_pipe.tokenizer.model_max_length      ,					truncation=_lowerCamelCase      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =text_inputs["input_ids"].to(_lowerCamelCase )

										lowerCamelCase__   =ldmad_pipe.text_encoder(_lowerCamelCase )[0]

										lowerCamelCase__   =prompt_embeds

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4






				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=_lowerCamelCase )
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   ="french fries"
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase      ,					negative_prompt=_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
										lowerCamelCase__   =np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1].flatten()
										lowerCamelCase__   =rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512)

										lowerCamelCase__   =np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
										lowerCamelCase__   =np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
										assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 50,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_9_5_5_8_6
										lowerCamelCase__   =0.3_3_7_9_5_5_1_5
										lowerCamelCase__   =1_1_2.4_8_5_1_8
										lowerCamelCase__   =9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_1_9_4_1_2_7
										lowerCamelCase__   =0.3_5_3_7_5_5_8_6
										lowerCamelCase__   =0.5_6_3_8_5_0_2
										lowerCamelCase__   =0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512, 1)
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3


